Bibliographic record
Abstract
Introduction: Hemodialysis patients have physical vulnerability due to the progressive decline of the body's systems. The physical changes in the patients can lead to haemostatics failure, known as frailty syndrome. The frail condition can cause maladaptive psychological responses due to changes that can cause anxiety in patients.Objective: This study aims to determine the prevalence of frailty in hemodialysis patients and to find out the different factor that correlates in frailty patients undergoing hemodialysis therapy.Method: This is a cross-sectional study involving 55 respondents who underwent hemodialysis therapy at the Jember Klinik Hospital. The instruments used in this study were the Edmonton Frail Scale (EFS) and the Hamilton Anxiety Rating Scale. Results: Moderate frailty was found in 23 patients (41.8%), followed by mild frailty in 18 patients (32.7%) and severe frailty in 14 patients (25.5%). In terms of anxiety, 18 patients (32.7%) had severe anxiety, 20 had moderate anxiety (36.4%), 12 (21.8%) had mild anxiety, and the other 7 (12.7%) were not anxious. The statistical analysis shows a significant difference in the average level of anxiety and the frailty status (p<0.001) among the patients. Conclusion: Frailty was highly prevalent in hemodialysis patients and had relations with anxiety and different sociodemographic characteristics of patients. The assessment of frailty should be considered when clinicians intervene and prevent worsening in patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".